Stepping stones and challenges in the use of artificial intelligence in the diagnosis of echinococcosis
Authors: Tommaso Manciulli, Enrico Brunetti
Journal: The Lancet Digital Health (2023)

In The Lancet Digital Health, two studies assess the potential role of artificial intelligence (AI) tools for the diagnosis of cystic echinococcosis and alveolar echinococcosis. Both cystic echinococcosis and alveolar echinococcosis are neglected tropical diseases that are frequently misdiagnosed, whose diagnosis classically relies on the use of ultrasound and often depends on operator experience.1Budke CM Casulli A Kern P Vuitton DA Cystic and alveolar echinococcosis: successes and continuing challenges.PLoS Negl Trop Dis. 2017; 11e0005477Crossref Scopus (49) Google Scholar Hence, new tools for the radiological diagnosis of cystic echinococcosis and alveolar echinococcosis are urgently needed. Previous studies on the use of AI in the field have involved building tools that could provide automatic segmentation of cystic echinococcosis lesions or have focused on the staging of cystic echinococcosis lesions according to the WHO Informal Working Group on Echinococcosis (WHO-IWGE) classification or some derivation thereof.2Xin S Shi H Jide A Zhu M Ma C Liao H Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network.Med Biol Eng Comput. 2020; 58: 659-668Crossref PubMed Scopus (7) Google Scholar, 3Wu M Yan C Wang X Liu Q Liu Z Song T Automatic classification of hepatic cystic echinococcosis using ultrasound images and deep learning.J Ultrasound Med. 2022; 41: 163-174Crossref PubMed Scopus (9) Google Scholar However, a comprehensive AI tool should be able to differentiate both diseases from other lesions, including both benign lesions such as simple cysts or hemangiomas, as well as malignant ones such as hepatic tumours. The distinction of the two parasitic diseases is an issue in co-endemic areas, mostly in China.4Deplazes P Rinaldi L Alvarez Rojas CA et al.Global distribution of alveolar and cystic echinococcosis.in: Thompson RCA Deplazes P Lymbery AJ Advances in parasitology: volume 95, Echinococcus and echinococcosis, part A. Elsevier, London2017: 315-493Crossref Scopus (591) Google Scholar The study by Zhan Wang and colleagues5Wang Z Bian H Li J et al.Detection and subtyping of hepatic echinococcosis from plain CT images with deep learning: a retrospective, multicentre study.Lancet Digit Health. 2023; (published online Sept 26.)https://doi.org/10.1016/S2589-7500(23)00136-XGoogle Scholar published in The Lancet Digital Health explores the use of an AI tool for the automatic detection of cystic echinococcosis and alveolar echinococcosis. Another study recently published in The Lancet Digital Health by Yongfeng Yang and colleagues6Yang Y Cairang Y Jiang T et al.Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study.Lancet Digit Health. 2023; 5: e503-e514Summary Full Text Full Text PDF PubMed Google Scholar assessed an AI tool based on ultrasound still images for the diagnosis and staging of cystic echinococcosis and for the diagnosis of alveolar echinococcosis. Both studies were carried out in China. Wang and colleagues report that the echinococcosis diagnostic AI system (EDAM) was able to distinguish between alveolar echinococcosis, cystic echinococcosis, and hepatic cyst lesions with good accuracy and specificity, while it had a low sensitivity for hepatic cysts. The system performed comparably to radiologists trained in the diagnosis of cystic echinococcosis and alveolar echinococcosis. However, the authors only included non-parasitic hepatic cysts in the dataset among non-echinococcosis patients; they did not include solid lesions (both benign and malignant) and fluid-filled lesions other than hepatic cysts. This is particularly relevant for both cystic echinococcosis and alveolar echinococcosis, given that alveolar echinococcosis lesions tend to have a solid appearance and that several stages of the WHO-IWGE classification (ie, stages CE3b to CE5) have solid components. Moreover, both solid and cystic tumours of the liver are often mistaken for cystic echinococcosis (namely the CE2 stage) or alveolar echinococcosis. The lack of such lesions in the dataset is a weakness, although the authors did test EDAM on contrast-enhanced images with good performances, suggesting that several neoplastic lesions could be included in the system. In contrast, the study by Yang and colleagues included both benign and malignant liver lesions in the dataset.6Yang Y Cairang Y Jiang T et al.Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study.Lancet Digit Health. 2023; 5: e503-e514Summary Full Text Full Text PDF PubMed Google Scholar The applicability of EDAM might also be hindered by its dependency on CT, an imaging technique that is often unavailable in endemic areas (eg, due to financial barriers, a paucity of trained staff, and logistical barriers on the patient's side) and that underperforms when it comes to staging cystic echinococcosis; Stojkovic and colleagues showed that CT scan is the least accurate method for diagnosing and staging cystic echinococcosis, and is outperformed by MRI and ultrasound.7Stojkovic M Rosenberger K Kauczor HU Junghanss T Hosch W Diagnosing and staging of cystic echinococcosis: how do CT and MRI perform in comparison to ultrasound?.PLoS Negl Trop Dis. 2012; 6e1880Crossref PubMed Scopus (108) Google Scholar The lack of cyst staging in cystic echinococcosis lesions is also an issue as they should be managed with a stage-specific approach as stated by the WHO-IWGE expert consensus, and failing to take this into account could result in overtreatment.8Brunetti E Kern P Vuitton DA Expert consensus for the diagnosis and treatment of cystic and alveolar echinococcosis in humans.Acta Trop. 2010; 114: 1-16Crossref PubMed Scopus (1337) Google Scholar Hence, cyst staging should be implemented as well. Population screening for echinococcosis has traditionally been carried out with ultrasound-based surveys, which potentially render an ultrasound-based tool a more attractive alternative.9Tamarozzi F Akhan O Cretu CM et al.Prevalence of abdominal cystic echinococcosis in rural Bulgaria, Romania, and Turkey: a cross-sectional, ultrasound-based, population study from the HERACLES project.Lancet Infect Dis. 2018; 18: 769-778Summary Full Text Full Text PDF PubMed Scopus (85) Google Scholar, 10MacPherson CN Romig T Zeyhle E Rees PH Were JB Portable ultrasound scanner versus serology in screening for hydatid cysts in a nomadic population.Lancet. 1987; 2: 259-261Summary PubMed Scopus (0) Google Scholar Wang and colleagues state that the use of ultrasound is operator-dependent and that an AI system based on this technology would be influenced by the need for an experienced operator performing image acquisition. However, Yang and colleagues show that still images can be used to train an AI system to diagnose cystic echinococcosis and alveolar echinococcosis with results comparable to if not outmatching experienced radiologists in hyperendemic areas.6Yang Y Cairang Y Jiang T et al.Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study.Lancet Digit Health. 2023; 5: e503-e514Summary Full Text Full Text PDF PubMed Google Scholar Both teams showed how AI can also be used to assist radiologists, as less experienced physicians were able to more correctly diagnose cystic echinococcosis and alveolar echinococcosis after being provided the results of AI readings.5Wang Z Bian H Li J et al.Detection and subtyping of hepatic echinococcosis from plain CT images with deep learning: a retrospective, multicentre study.Lancet Digit Health. 2023; (published online Sept 26.)https://doi.org/10.1016/S2589-7500(23)00136-XGoogle Scholar, 6Yang Y Cairang Y Jiang T et al.Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study.Lancet Digit Health. 2023; 5: e503-e514Summary Full Text Full Text PDF PubMed Google Scholar For all these reasons, the ultrasound-based tool should be tested in a real-life setting (ie, during mass screening campaigns). Since still images present limitations, further studies should develop tools based on the use of dynamic images to reproduce current clinical practice as the use of still images is known to reduce diagnostic accuracy; in fact, the ultrasound-based AI tool became less reliable when comparing CE2 cysts with cystoadenomas.6Yang Y Cairang Y Jiang T et al.Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study.Lancet Digit Health. 2023; 5: e503-e514Summary Full Text Full Text PDF PubMed Google Scholar Future tools based on ultrasound should thus be implemented with the use of dynamic images in mind. In contrast, the EDAM tool based on CT images should be further developed by testing its capabilities on contrast-enhanced images as well as on non-cystic echinococcosis and non-alveolar echinococcosis lesions other than simple cysts, and by introducing cystic echinococcosis staging. We declare no competing interests. We thank Sam Goblirsch for reviewing the paper for English grammar and style. Detection and subtyping of hepatic echinococcosis from plain CT images with deep learning: a retrospective, multicentre studyEDAM is a clinically applicable AI system that can provide patient-level diagnoses with interpretable results. The accuracy and generalisation ability of EDAM demonstrates its potential for clinical use, especially in underdeveloped areas. Full-Text PDF Open Access